CavinHuang-comfyui-nodes-docs/docs/LayerUtility%3A ImageBlend.md

Documentation

• Class name: ImageBlend
• Category: 😺dzNodes/LayerUtility
• Output node: False
• Repo Ref: https://github.com/chflame163/ComfyUI_LayerStyle

一个用于合成图层的简单节点，提供多种混合模式供选择，可设置透明度。

Input types

Required

• background_image

  • 背景图像。
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor

• layer_image

  • 用于合成的层图像。
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor

• invert_mask

  • 是否反转遮罩。
  • Comfy dtype: BOOLEAN
  • Python dtype: bool

• blend_mode

  • 图层混合模式。
  • Comfy dtype: ENUM
  • Python dtype: str

• opacity

  • 不透明度。
  • Comfy dtype: INT
  • Python dtype: int

Optional

• layer_mask
  • 层图像的遮罩。
  • Comfy dtype: MASK
  • Python dtype: torch.Tensor

Output types

• image
  • 合成后的图像。
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor

Usage tips

• Infra type: CPU

Source code

class ImageBlend:

    def __init__(self):
        pass

    @classmethod
    def INPUT_TYPES(self):

        return {
            "required": {
                "background_image": ("IMAGE", ),  #
                "layer_image": ("IMAGE",),  #
                "invert_mask": ("BOOLEAN", {"default": True}),  # 反转mask
                "blend_mode": (chop_mode,),  # 混合模式
                "opacity": ("INT", {"default": 100, "min": 0, "max": 100, "step": 1}),  # 透明度
            },
            "optional": {
                "layer_mask": ("MASK",),  #
            }
        }

    RETURN_TYPES = ("IMAGE",)
    RETURN_NAMES = ("image",)
    FUNCTION = 'image_blend'
    CATEGORY = '😺dzNodes/LayerUtility'

    def image_blend(self, background_image, layer_image,
                  invert_mask, blend_mode, opacity,
                  layer_mask=None
                  ):

        b_images = []
        l_images = []
        l_masks = []
        ret_images = []
        for b in background_image:
            b_images.append(torch.unsqueeze(b, 0))
        for l in layer_image:
            l_images.append(torch.unsqueeze(l, 0))
            m = tensor2pil(l)
            if m.mode == 'RGBA':
                l_masks.append(m.split()[-1])
            else:
                l_masks.append(Image.new('L', m.size, 'white'))
        if layer_mask is not None:
            if layer_mask.dim() == 2:
                layer_mask = torch.unsqueeze(layer_mask, 0)
            l_masks = []
            for m in layer_mask:
                if invert_mask:
                    m = 1 - m
                l_masks.append(tensor2pil(torch.unsqueeze(m, 0)).convert('L'))
        max_batch = max(len(b_images), len(l_images), len(l_masks))
        for i in range(max_batch):
            background_image = b_images[i] if i < len(b_images) else b_images[-1]
            layer_image = l_images[i] if i < len(l_images) else l_images[-1]
            _mask = l_masks[i] if i < len(l_masks) else l_masks[-1]

            _canvas = tensor2pil(background_image).convert('RGB')
            _layer = tensor2pil(layer_image).convert('RGB')

            if _mask.size != _layer.size:
                _mask = Image.new('L', _layer.size, 'white')
                log(f"Warning: {NODE_NAME} mask mismatch, dropped!", message_type='warning')

            # 合成layer
            _comp = chop_image(_canvas, _layer, blend_mode, opacity)
            _canvas.paste(_comp, mask=_mask)

            ret_images.append(pil2tensor(_canvas))
        log(f"{NODE_NAME} Processed {len(ret_images)} image(s).", message_type='finish')
        return (torch.cat(ret_images, dim=0),)